During the first two runs of the CERN Large Hadron Collider (LHC) the experimental collaborations
have accumulated a vast amount of high-quality data of proton–proton collisions at energies as high as 13 TeV. In the
current shutdown period, the collaborations refine their search and measurement strategies, in order to
fully exploit the physics potential of the acquired data, and to prepare for the upcoming third run of the LHC.
However, no significant increase in collision energy is planned for the foreseeable future. Accordingly,
the focus of the theory and experimental communities should be on devising new effective and robust
analysis techniques to interrogate the data, so that no stone is left unturned. In this context, the use of
deep-learning algorithms to augment performance is now becoming standard practice
(see e.g. [1]}, [2]}, [3]} for recent reviews).
Complementary to this effort, measurement campaigns that deliver unfolded data that can be compared to
state-of-the-art theoretical calculations have been, and will further be, pursued in order to stress-test
our understanding of the Standard Model.
